Camille Noufi

musician - engineer - explorer

Portfolio

Projects

Musical Works

Research

About

I am fascinated by the connection between the arts, technology and their impact on the human experience. Growing up in the beautiful state of Colorado, I was immersed in music, science and nature. From a young age I have observed the scientific side of arts, the artistic side of science, and their ability to help us foster deeper connections to ourselves as well as to others and the world around us.

In order to gain a deeper understanding of this connection, I obtained a degree with honors in Electrical & Computer Engineering with an emphasis in signal processing from the University of Colorado, Boulder. Additionally, I spent two years as a voice major in the College of Music and directed a premier collegiate a cappella group.

I am now combining my knowledge in both fields to explore tangible ways music and sound technologies can further social connection. My current work focuses on speech and singing signal processing.

Check out my portfolio above for an in-depth look into my work experience, research, compositions and other areas of interest! Also feel free to reach out via email about anything. I always enjoy connecting with new people!

Projects

'RotoVision' Hologram Prototype

For our Senior Capstone project, my team "MetaTAIT" and I built a prototype hologram for Tait Towers, seeking to provide a physical proof-of-concept of creating a 3D image by exploiting persistence of vision and stereoscopy. The final prototype uses 18 blinded LED strips grouped in sections of 6 around the circumference of the rotating structure. As the machine spins, the microcontrollers process the data image data to create 18 different 2D images that are only visible from certain discrete viewing angles. Together, these discrete 2D images radially stitch together to form a 3D object in the observer's mind.

My key contributions to the project included designing the algorithms for data handling and image processing, embedded software design and handling data transfer.
My team and I developed the requirements, specifications, build process, and test process for the prototype, and had a working prototype to display at our Senior Design Exposition and give to Tait Towers for further research.This was one of the most challenging projects I have worked on due to its open-ended nature, and because of that, one of the most rewarding.

MIR Genre Classifier

The goal of this project was to pairing the right feature extraction algorithms with the right classifiers in order to best predict a song's genre based on its audio file. I extracted MFCC and Pitch Class data from each song, setting up the frequency bins to be logarithmic to more closely align with the way humans interpret musical pitches. I used this time-frequency-based data (containing insight into melodic progression, rhythm and timbre) from each song to determine each song’s relationship to every other song in the set.

Using this metadata, I built a simple cluster classifier and SVM classifier that attempted to predict a song's genre. I found that MFCCs work well to classify certain genres (electronic, classical, jazz), confused other genres with each other (rock, pop) and have a hard time deciding what to do with world music because it can relate to a variety of the other genres either in melodic spectrum, rhythmic tendencies or timbre.

This project has lead to independent research on a cappella singing style identification! Check it out under in my research portfolio!

Created For:
ECE Theory and Applications of Digital Filtering

Date:
December 2016

'SmartPiano' Bluetooth Keyboard and App

My goal was to create a wireless portable keyboard and supporting smartphone application that could be used in a variety of locations. Based on previous experience and various interviews, my partner and I learned that many musicians desire a more portable instrument that also allows volume control. So, we decided to design a touch-sensitive piano keyboard that transmits data via Bluetooth to a smartphone, which in turn plays the pitch corresponding to the key tapped.

The implementation utilizes conducting metal keys, an Arduino Nano to process key contact, an ARM LPC1115 microcontroller to interact with the Bluetooth module and an Android smartphone application to play the pitches and give other visual feedback. The end prototype is a small, five key, Bluetooth “piano” that anyone can carry around and play anywhere.I would like to improve this project by developing a corresponding iOS app, creating a cleaner keyboard interface, and expand the keys to a complete octave.

I would love to hear any questions or ideas you may have about the project!

Please contact me directly to learn more about my work at Lincoln Laboratory!

Genre Classification Via Harmonic Analysis

I worked with an interdisciplinary team at CU Boulder led by Dr. Kris Shaffer conducting research on techniques to improve musical genre classification. Using the McGill Billboard dataset, we analyzed the harmonic structure of songs and trained models to fit genres to harmonic progression.